BhuvaneshWadhwani_A0199148M_FinalProject
Bhuvanesh Wadhwani
2022-10-27
#Calling packages
packages_to_use<- c("ROCR", "tidyverse", "caret", "glmnet", "rpart", "rpart.plot", "vip", "pdp", "randomForest", "gbm", "GGally", "cowplot", "dplyr", "DALEX", "DALEXtra", "lime", "localModel")
for(i in packages_to_use){
if( ! i %in% rownames(installed.packages()) ) {
print(paste(i, "not installed; installing now:\n") )
install.packages(i)
}
require(i, character.only = TRUE)
}#Taking in data
sact.dat <- read.csv("sac.csv")
#combining variables
sact.dat <- sact.dat %>%
mutate(paredu = (Medu + Fedu) ) %>%
mutate(grades = (G1 + G2 + G3)/3)
head(sact.dat)## school sex age address famsize Pstatus Medu Fedu Mjob Fjob reason
## 1 GP F 18 U GT3 A 4 4 at_home teacher course
## 2 GP F 17 U GT3 T 1 1 at_home other course
## 3 GP F 15 U LE3 T 1 1 at_home other other
## 4 GP F 15 U GT3 T 4 2 health services home
## 5 GP F 16 U GT3 T 3 3 other other home
## 6 GP M 16 U LE3 T 4 3 services other reputation
## guardian traveltime studytime failures schoolsup famsup paid activities
## 1 mother 2 2 0 yes no no no
## 2 father 1 2 0 no yes no no
## 3 mother 1 2 0 yes no no no
## 4 mother 1 3 0 no yes no yes
## 5 father 1 2 0 no yes no no
## 6 mother 1 2 0 no yes no yes
## nursery higher internet romantic famrel freetime goout Dalc Walc health
## 1 yes yes no no 4 3 4 1 1 3
## 2 no yes yes no 5 3 3 1 1 3
## 3 yes yes yes no 4 3 2 2 3 3
## 4 yes yes yes yes 3 2 2 1 1 5
## 5 yes yes no no 4 3 2 1 2 5
## 6 yes yes yes no 5 4 2 1 2 5
## absences G1 G2 G3 paredu grades
## 1 4 0 11 11 8 7.333333
## 2 2 9 11 11 2 10.333333
## 3 6 12 13 12 2 12.333333
## 4 0 14 14 14 6 14.000000
## 5 0 11 13 13 6 12.333333
## 6 6 12 12 13 7 12.333333psych::describe(sact.dat)## vars n mean sd median trimmed mad min max range skew
## school* 1 649 1.35 0.48 1.00 1.31 0.00 1.00 2.00 1.00 0.64
## sex* 2 649 1.41 0.49 1.00 1.39 0.00 1.00 2.00 1.00 0.37
## age 3 649 16.74 1.22 17.00 16.70 1.48 15.00 22.00 7.00 0.41
## address* 4 649 1.70 0.46 2.00 1.74 0.00 1.00 2.00 1.00 -0.85
## famsize* 5 649 1.30 0.46 1.00 1.25 0.00 1.00 2.00 1.00 0.89
## Pstatus* 6 649 1.88 0.33 2.00 1.97 0.00 1.00 2.00 1.00 -2.29
## Medu 7 649 2.51 1.13 2.00 2.53 1.48 0.00 4.00 4.00 -0.03
## Fedu 8 649 2.31 1.10 2.00 2.27 1.48 0.00 4.00 4.00 0.21
## Mjob* 9 649 2.94 1.25 3.00 2.93 1.48 1.00 5.00 4.00 -0.19
## Fjob* 10 649 3.22 0.86 3.00 3.29 0.00 1.00 5.00 4.00 -0.53
## reason* 11 649 2.11 1.19 2.00 2.02 1.48 1.00 4.00 3.00 0.56
## guardian* 12 649 1.83 0.52 2.00 1.83 0.00 1.00 3.00 2.00 -0.20
## traveltime 13 649 1.57 0.75 1.00 1.43 0.00 1.00 4.00 3.00 1.24
## studytime 14 649 1.93 0.83 2.00 1.85 1.48 1.00 4.00 3.00 0.70
## failures 15 649 0.22 0.59 0.00 0.07 0.00 0.00 3.00 3.00 3.08
## schoolsup* 16 649 1.10 0.31 1.00 1.01 0.00 1.00 2.00 1.00 2.57
## famsup* 17 649 1.61 0.49 2.00 1.64 0.00 1.00 2.00 1.00 -0.46
## paid* 18 649 1.06 0.24 1.00 1.00 0.00 1.00 2.00 1.00 3.69
## activities* 19 649 1.49 0.50 1.00 1.48 0.00 1.00 2.00 1.00 0.06
## nursery* 20 649 1.80 0.40 2.00 1.88 0.00 1.00 2.00 1.00 -1.52
## higher* 21 649 1.89 0.31 2.00 1.99 0.00 1.00 2.00 1.00 -2.55
## internet* 22 649 1.77 0.42 2.00 1.83 0.00 1.00 2.00 1.00 -1.26
## romantic* 23 649 1.37 0.48 1.00 1.34 0.00 1.00 2.00 1.00 0.55
## famrel 24 649 3.93 0.96 4.00 4.05 1.48 1.00 5.00 4.00 -1.10
## freetime 25 649 3.18 1.05 3.00 3.19 1.48 1.00 5.00 4.00 -0.18
## goout 26 649 3.18 1.18 3.00 3.20 1.48 1.00 5.00 4.00 -0.01
## Dalc 27 649 1.50 0.92 1.00 1.28 0.00 1.00 5.00 4.00 2.13
## Walc 28 649 2.28 1.28 2.00 2.14 1.48 1.00 5.00 4.00 0.63
## health 29 649 3.54 1.45 4.00 3.67 1.48 1.00 5.00 4.00 -0.50
## absences 30 649 3.66 4.64 2.00 2.80 2.97 0.00 32.00 32.00 2.01
## G1 31 649 11.40 2.75 11.00 11.38 2.97 0.00 19.00 19.00 0.00
## G2 32 649 11.57 2.91 11.00 11.56 2.97 0.00 19.00 19.00 -0.36
## G3 33 649 11.91 3.23 12.00 12.04 2.97 0.00 19.00 19.00 -0.91
## paredu 34 649 4.82 2.03 5.00 4.79 2.97 0.00 8.00 8.00 0.12
## grades 35 649 11.63 2.83 11.67 11.64 2.47 1.33 18.67 17.33 -0.23
## kurtosis se
## school* -1.60 0.02
## sex* -1.87 0.02
## age 0.05 0.05
## address* -1.28 0.02
## famsize* -1.21 0.02
## Pstatus* 3.23 0.01
## Medu -1.27 0.04
## Fedu -1.12 0.04
## Mjob* -0.83 0.05
## Fjob* 1.17 0.03
## reason* -1.25 0.05
## guardian* 0.17 0.02
## traveltime 1.08 0.03
## studytime 0.02 0.03
## failures 9.70 0.02
## schoolsup* 4.64 0.01
## famsup* -1.79 0.02
## paid* 11.66 0.01
## activities* -2.00 0.02
## nursery* 0.31 0.02
## higher* 4.50 0.01
## internet* -0.41 0.02
## romantic* -1.71 0.02
## famrel 1.32 0.04
## freetime -0.41 0.04
## goout -0.87 0.05
## Dalc 4.28 0.04
## Walc -0.78 0.05
## health -1.13 0.06
## absences 5.70 0.18
## G1 0.02 0.11
## G2 1.63 0.11
## G3 2.66 0.13
## paredu -1.14 0.08
## grades 0.58 0.11#selecting columns of interest based on low skewness
sapply(sact.dat, var)## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion## school sex age address famsize Pstatus Medu
## NA NA 1.4838593 NA NA NA 1.2872082
## Fedu Mjob Fjob reason guardian traveltime studytime
## 1.2098480 NA NA NA NA 0.5604919 0.6880861
## failures schoolsup famsup paid activities nursery higher
## 0.3519279 NA NA NA NA NA NA
## internet romantic famrel freetime goout Dalc Walc
## NA NA 0.9133948 1.1047956 1.3824260 0.8553187 1.6496319
## health absences G1 G2 G3 paredu grades
## 2.0916652 21.5366423 7.5364806 8.4892903 10.4371398 4.1130657 8.0279305sacf.dat <- select(sact.dat, Mjob, Fjob, guardian, famsup, activities, romantic, freetime, health, goout, paredu, sex, age, reason, grades)
print(head(sacf.dat))## Mjob Fjob guardian famsup activities romantic freetime health goout
## 1 at_home teacher mother no no no 3 3 4
## 2 at_home other father yes no no 3 3 3
## 3 at_home other mother no no no 3 3 2
## 4 health services mother yes yes yes 2 5 2
## 5 other other father yes no no 3 5 2
## 6 services other mother yes yes no 4 5 2
## paredu sex age reason grades
## 1 8 F 18 course 7.333333
## 2 2 F 17 course 10.333333
## 3 2 F 15 other 12.333333
## 4 6 F 15 home 14.000000
## 5 6 F 16 home 12.333333
## 6 7 M 16 reputation 12.333333print(str(sacf.dat))## 'data.frame': 649 obs. of 14 variables:
## $ Mjob : chr "at_home" "at_home" "at_home" "health" ...
## $ Fjob : chr "teacher" "other" "other" "services" ...
## $ guardian : chr "mother" "father" "mother" "mother" ...
## $ famsup : chr "no" "yes" "no" "yes" ...
## $ activities: chr "no" "no" "no" "yes" ...
## $ romantic : chr "no" "no" "no" "yes" ...
## $ freetime : int 3 3 3 2 3 4 4 1 2 5 ...
## $ health : int 3 3 3 5 5 5 3 1 1 5 ...
## $ goout : int 4 3 2 2 2 2 4 4 2 1 ...
## $ paredu : int 8 2 2 6 6 7 4 8 5 7 ...
## $ sex : chr "F" "F" "F" "F" ...
## $ age : int 18 17 15 15 16 16 16 17 15 15 ...
## $ reason : chr "course" "course" "other" "home" ...
## $ grades : num 7.33 10.33 12.33 14 12.33 ...
## NULL##–Details about the dataset: Student Alcohol Consumption–##
#school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira)
#sex - student’s sex (binary: ‘F’ - female or ‘M’ - male)
#age - student’s age (numeric: from 15 to 22)
#address - student’s home address type (binary: ‘U’ - urban or ‘R’ - rural)
#famsize - family size (binary: ‘LE3’ - less or equal to 3 or ‘GT3’ - greater than 3)
#Pstatus - parent’s cohabitation status (binary: ‘T’ - living together or ‘A’ - apart)
#Medu - mother’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
#Fedu - father’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
#Mjob - mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
#Fjob - father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
#reason - reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’)
#guardian - student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’)
#traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
#studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
#failures - number of past class failures (numeric: n if 1<=n<3, else 4)
#schoolsup - extra educational support (binary: yes or no)
#famsup - family educational support (binary: yes or no)
#paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
#activities - extra-curricular activities (binary: yes or no)
#nursery - attended nursery school (binary: yes or no)
#higher - wants to take higher education (binary: yes or no)
#internet - Internet access at home (binary: yes or no)
#romantic - with a romantic relationship (binary: yes or no)
#famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
#freetime - free time after school (numeric: from 1 - very low to 5 - very high)
#goout - going out with friends (numeric: from 1 - very low to 5 - very high)
#Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
#Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
#health - current health status (numeric: from 1 - very bad to 5 - very good)
#absences - number of school absences (numeric: from 0 to 93)
#G1 - first period grade (numeric: from 0 to 20)
#G2 - second period grade (numeric: from 0 to 20)
#G3 - final grade (numeric: from 0 to 20, output target)
##added variables:
#paredu - parent’s education out of 8 (higher = higher combined education levels of both parents)
#grade - mean grade (numeric: from 0 to 20)
#Factoring variables in the dataset
#sex
sacf.dat$sex <- factor(sacf.dat$sex,
levels = c("F", "M"),
labels = c("Female", "Male"))
#Mjob
sacf.dat$Mjob <- factor(sacf.dat$Mjob,
levels = c("teacher", "health","services","at_home","other"),
labels = c("teacher", "health","services","at_home","other"))
#Fjob
sacf.dat$Fjob <- factor(sacf.dat$Fjob,
levels = c("teacher", "health","services","at_home","other"),
labels = c("teacher", "health","services","at_home","other"))
#guardian
sacf.dat$guardian <- factor(sacf.dat$guardian,
levels = c("mother", "father","other"),
labels = c("mother", "father","other"))
#famsup
sacf.dat$famsup <- factor(sacf.dat$famsup,
levels = c("no", "yes"),
labels = c("no", "yes"))
#activities
sacf.dat$activities <- factor(sacf.dat$activities,
levels = c("no", "yes"),
labels = c("no", "yes"))
#romantic
sacf.dat$romantic <- factor(sacf.dat$romantic,
levels = c("no", "yes"),
labels = c("no", "yes"))
#freetime
sacf.dat$freetime <- factor(sacf.dat$freetime,
levels = c("1","2","3","4","5"),
labels = c("1","2","3","4","5"))
#goout
sacf.dat$goout <- factor(sacf.dat$goout,
levels = c("1","2","3","4", "5"),
labels = c("1","2","3","4", "5"))
#reason
sacf.dat$reason <- factor(sacf.dat$reason,
levels = c("home", "reputation","course","other"),
labels = c("home", "reputation","course","other"))
#health
sacf.dat$health <- factor(sacf.dat$health,
levels = c("1","2","3","4","5"),
labels = c("1","2","3","4","5"))head(sacf.dat)## Mjob Fjob guardian famsup activities romantic freetime health goout
## 1 at_home teacher mother no no no 3 3 4
## 2 at_home other father yes no no 3 3 3
## 3 at_home other mother no no no 3 3 2
## 4 health services mother yes yes yes 2 5 2
## 5 other other father yes no no 3 5 2
## 6 services other mother yes yes no 4 5 2
## paredu sex age reason grades
## 1 8 Female 18 course 7.333333
## 2 2 Female 17 course 10.333333
## 3 2 Female 15 other 12.333333
## 4 6 Female 15 home 14.000000
## 5 6 Female 16 home 12.333333
## 6 7 Male 16 reputation 12.333333str(sacf.dat)## 'data.frame': 649 obs. of 14 variables:
## $ Mjob : Factor w/ 5 levels "teacher","health",..: 4 4 4 2 5 3 5 5 3 5 ...
## $ Fjob : Factor w/ 5 levels "teacher","health",..: 1 5 5 3 5 5 5 1 5 5 ...
## $ guardian : Factor w/ 3 levels "mother","father",..: 1 2 1 1 2 1 1 1 1 1 ...
## $ famsup : Factor w/ 2 levels "no","yes": 1 2 1 2 2 2 1 2 2 2 ...
## $ activities: Factor w/ 2 levels "no","yes": 1 1 1 2 1 2 1 1 1 2 ...
## $ romantic : Factor w/ 2 levels "no","yes": 1 1 1 2 1 1 1 1 1 1 ...
## $ freetime : Factor w/ 5 levels "1","2","3","4",..: 3 3 3 2 3 4 4 1 2 5 ...
## $ health : Factor w/ 5 levels "1","2","3","4",..: 3 3 3 5 5 5 3 1 1 5 ...
## $ goout : Factor w/ 5 levels "1","2","3","4",..: 4 3 2 2 2 2 4 4 2 1 ...
## $ paredu : int 8 2 2 6 6 7 4 8 5 7 ...
## $ sex : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 2 2 1 2 2 ...
## $ age : int 18 17 15 15 16 16 16 17 15 15 ...
## $ reason : Factor w/ 4 levels "home","reputation",..: 3 3 4 1 1 2 1 1 1 1 ...
## $ grades : num 7.33 10.33 12.33 14 12.33 ...summary(sacf.dat)## Mjob Fjob guardian famsup activities romantic
## teacher : 72 teacher : 36 mother:455 no :251 no :334 no :410
## health : 48 health : 23 father:153 yes:398 yes:315 yes:239
## services:136 services:181 other : 41
## at_home :135 at_home : 42
## other :258 other :367
##
## freetime health goout paredu sex age
## 1: 45 1: 90 1: 48 Min. :0.000 Female:383 Min. :15.00
## 2:107 2: 78 2:145 1st Qu.:3.000 Male :266 1st Qu.:16.00
## 3:251 3:124 3:205 Median :5.000 Median :17.00
## 4:178 4:108 4:141 Mean :4.821 Mean :16.74
## 5: 68 5:249 5:110 3rd Qu.:6.000 3rd Qu.:18.00
## Max. :8.000 Max. :22.00
## reason grades
## home :149 Min. : 1.333
## reputation:143 1st Qu.:10.000
## course :285 Median :11.667
## other : 72 Mean :11.625
## 3rd Qu.:13.333
## Max. :18.667psych::describe(sacf.dat) #skew is high for guardian, Mjob, Fjob. ## vars n mean sd median trimmed mad min max range skew
## Mjob* 1 649 3.71 1.35 4.00 3.88 1.48 1.00 5.00 4.00 -0.73
## Fjob* 2 649 4.05 1.22 5.00 4.24 0.00 1.00 5.00 4.00 -0.95
## guardian* 3 649 1.36 0.60 1.00 1.25 0.00 1.00 3.00 2.00 1.43
## famsup* 4 649 1.61 0.49 2.00 1.64 0.00 1.00 2.00 1.00 -0.46
## activities* 5 649 1.49 0.50 1.00 1.48 0.00 1.00 2.00 1.00 0.06
## romantic* 6 649 1.37 0.48 1.00 1.34 0.00 1.00 2.00 1.00 0.55
## freetime* 7 649 3.18 1.05 3.00 3.19 1.48 1.00 5.00 4.00 -0.18
## health* 8 649 3.54 1.45 4.00 3.67 1.48 1.00 5.00 4.00 -0.50
## goout* 9 649 3.18 1.18 3.00 3.20 1.48 1.00 5.00 4.00 -0.01
## paredu 10 649 4.82 2.03 5.00 4.79 2.97 0.00 8.00 8.00 0.12
## sex* 11 649 1.41 0.49 1.00 1.39 0.00 1.00 2.00 1.00 0.37
## age 12 649 16.74 1.22 17.00 16.70 1.48 15.00 22.00 7.00 0.41
## reason* 13 649 2.43 0.96 3.00 2.41 1.48 1.00 4.00 3.00 -0.20
## grades 14 649 11.63 2.83 11.67 11.64 2.47 1.33 18.67 17.33 -0.23
## kurtosis se
## Mjob* -0.66 0.05
## Fjob* -0.18 0.05
## guardian* 0.95 0.02
## famsup* -1.79 0.02
## activities* -2.00 0.02
## romantic* -1.71 0.02
## freetime* -0.41 0.04
## health* -1.13 0.06
## goout* -0.87 0.05
## paredu -1.14 0.08
## sex* -1.87 0.02
## age 0.05 0.05
## reason* -1.04 0.04
## grades 0.58 0.11sum(is.na(sacf.dat)) #there are no missing values in the dataframe## [1] 0#ggpairs to look at overall patterns
plotgg1 <- GGally::ggpairs(sacf.dat,
progress = FALSE,
alpha = 0.2)## Warning in warn_if_args_exist(list(...)): Extra arguments: "alpha" are being
## ignored. If these are meant to be aesthetics, submit them using the 'mapping'
## variable within ggpairs with ggplot2::aes or ggplot2::aes_string.suppressWarnings(print(plotgg1))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
age.count <- ggplot(sacf.dat, aes(x = age, fill = age, color = age ) ) +
geom_histogram(alpha = 0.5, bins = 10) +
theme(legend.position = "right")
paredu.count <- ggplot(sacf.dat, aes(x = paredu, fill = paredu, color = paredu ) ) +
geom_histogram(alpha = 0.5, bins = 10) +
theme(legend.position = "right")
plot_grid(age.count, paredu.count, labels=c( "age", "paredu"), ncol = 2, nrow = 1)
num.dat <- select_if(sacf.dat, is.numeric)
head(num.dat)## paredu age grades
## 1 8 18 7.333333
## 2 2 17 10.333333
## 3 2 15 12.333333
## 4 6 15 14.000000
## 5 6 16 12.333333
## 6 7 16 12.333333age<- ggplot(sacf.dat, aes(x = grades, y = age)) +
geom_point(position = position_jitter(0.33), col = "red", alpha=0.3) +
geom_smooth(method = lm, se = FALSE,linetype ="dashed") +
#facet_wrap(~sex) +
theme(legend.position = "right")
paredu<- ggplot(sacf.dat, aes(x = grades, y = paredu )) +
geom_point(position = position_jitter(0.33), col = "green", alpha=0.3) +
geom_smooth(method = lm, se = FALSE,linetype ="dashed") +
#facet_wrap(~sex) +
theme(legend.position = "right")
plot_grid(age, paredu,labels=c("age", "paredu"), ncol = 2, nrow = 1)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
#the spread seems to be quite even, with a few outlierscat.dat <- select_if(sacf.dat, is.factor)
head(cat.dat)## Mjob Fjob guardian famsup activities romantic freetime health goout
## 1 at_home teacher mother no no no 3 3 4
## 2 at_home other father yes no no 3 3 3
## 3 at_home other mother no no no 3 3 2
## 4 health services mother yes yes yes 2 5 2
## 5 other other father yes no no 3 5 2
## 6 services other mother yes yes no 4 5 2
## sex reason
## 1 Female course
## 2 Female course
## 3 Female other
## 4 Female home
## 5 Female home
## 6 Male reputation#Histograms for each variables against grades
sex.plot <- ggplot(sacf.dat, aes(x = grades, fill = sex, color = sex ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
Mjob.plot <- ggplot(sacf.dat, aes(x = grades, fill = Mjob, color = Mjob ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
Fjob.plot <- ggplot(sacf.dat, aes(x = grades, fill = Fjob, color = Fjob ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
guardian.plot <- ggplot(sacf.dat, aes(x = grades, fill = guardian, color = guardian ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
famsup.plot <- ggplot(sacf.dat, aes(x = grades, fill = famsup, color = famsup ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
activities.plot <- ggplot(sacf.dat, aes(x = grades, fill = activities, color = activities ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
romantic.plot <- ggplot(sacf.dat, aes(x = grades, fill = romantic, color = romantic ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
freetime.plot <- ggplot(sacf.dat, aes(x = grades, fill = freetime, color = freetime ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
health.plot <- ggplot(sacf.dat, aes(x = grades, fill = health, color = health ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
goout.plot <- ggplot(sacf.dat, aes(x = grades, fill = goout, color = goout ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
reason.plot <- ggplot(sacf.dat, aes(x = grades, fill = reason, color = reason ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
age.plot <- ggplot(sacf.dat, aes(x = grades, fill = age, color = age ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
paredu.plot <- ggplot(sacf.dat, aes(x = grades, fill = paredu, color = paredu ) ) +
geom_histogram(alpha = 0.5, bins = 30) +
theme(legend.position = "right")
plot_grid(sex.plot, Mjob.plot, Fjob.plot, guardian.plot, famsup.plot, activities.plot, romantic.plot, freetime.plot, health.plot, goout.plot, reason.plot, age.plot, paredu.plot,
labels=c("sex", "Mjob","Fjob", "guardian", "famsup", "activities", "romantic", "freetime", "health", "goout", "reason", "age", "paredu"),
ncol = 3, nrow = 5)
sex.check <- ggplot(sacf.dat, aes(x = sex, fill = sex, color = sex ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsMjob.check <- ggplot(sacf.dat, aes(x = Mjob, fill = Mjob, color = Mjob ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsFjob.check <- ggplot(sacf.dat, aes(x = Fjob, fill = Fjob, color = Fjob ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsguardian.check <- ggplot(sacf.dat, aes(x = guardian, fill = guardian, color = guardian ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsfamsup.check <- ggplot(sacf.dat, aes(x = famsup, fill = famsup, color = famsup ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsactivities.check <- ggplot(sacf.dat, aes(x = activities, fill = activities, color = activities ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsromantic.check <- ggplot(sacf.dat, aes(x = romantic, fill = romantic, color = romantic ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsfreetime.check <- ggplot(sacf.dat, aes(x = freetime, fill = freetime, color = freetime ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binshealth.check <- ggplot(sacf.dat, aes(x = health, fill = health, color = health ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsgoout.check <- ggplot(sacf.dat, aes(x = goout, fill = goout, color = goout ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsreason.check <- ggplot(sacf.dat, aes(x = reason, fill = reason, color = reason ) ) +
geom_bar(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsplot_grid(sex.check, Mjob.check, Fjob.check, guardian.check, famsup.check, activities.check, romantic.check, freetime.check, health.check, goout.check, reason.check,
labels=c("sex", "Mjob","Fjob", "guardian", "famsup", "activities", "romantic", "freetime", "health", "goout", "reason"),
ncol = 3, nrow = 4)
#again we see how Mjob, Fjob, and guardian are skewed.
#health does look a little skewed, but the psych::description showed that it is fine, so we will trust that.#boxplots
sex.plot1 <- ggplot(sacf.dat, aes(y = grades, x = sex, fill = sex, color = sex ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsMjob.plot1 <- ggplot(sacf.dat, aes(y = grades, x = Mjob, fill = Mjob, color = Mjob ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsFjob.plot1 <- ggplot(sacf.dat, aes(y = grades, x = Fjob, fill = Fjob, color = Fjob ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsguardian.plot1 <- ggplot(sacf.dat, aes(y = grades, x = guardian, fill = guardian, color = guardian ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsfamsup.plot1 <- ggplot(sacf.dat, aes(y = grades, x = famsup, fill = famsup, color = famsup ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsactivities.plot1 <- ggplot(sacf.dat, aes(y = grades, x = activities, fill = activities, color = activities ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsromantic.plot1 <- ggplot(sacf.dat, aes(y = grades, x = romantic, fill = romantic, color = romantic ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsfreetime.plot1 <- ggplot(sacf.dat, aes(y = grades, x = freetime, fill = freetime, color = freetime ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binshealth.plot1 <- ggplot(sacf.dat, aes(y = grades, x = health, fill = health, color = health ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsgoout.plot1 <- ggplot(sacf.dat, aes(y = grades, x = goout, fill = goout, color = goout ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsreason.plot1 <- ggplot(sacf.dat, aes(y = grades, x = reason, fill = reason, color = reason ) ) +
geom_boxplot(alpha = 0.5, bins = 30) +
theme(legend.position = "right")## Warning: Ignoring unknown parameters: binsplot_grid(sex.plot1, Mjob.plot1, Fjob.plot1, guardian.plot1, famsup.plot1, activities.plot1, romantic.plot1, freetime.plot1, health.plot1, goout.plot1, reason.plot1,
labels=c("sex", "Mjob","Fjob", "guardian", "famsup", "activities", "romantic", "freetime", "health", "goout", "reason"),
ncol = 3, nrow = 4)
sacf.dat <- sacf.dat %>%
mutate(paredu.cat = case_when(paredu > 4 ~ "High",
paredu <= 4 ~ "Low",
))
head(sacf.dat)## Mjob Fjob guardian famsup activities romantic freetime health goout
## 1 at_home teacher mother no no no 3 3 4
## 2 at_home other father yes no no 3 3 3
## 3 at_home other mother no no no 3 3 2
## 4 health services mother yes yes yes 2 5 2
## 5 other other father yes no no 3 5 2
## 6 services other mother yes yes no 4 5 2
## paredu sex age reason grades paredu.cat
## 1 8 Female 18 course 7.333333 High
## 2 2 Female 17 course 10.333333 Low
## 3 2 Female 15 other 12.333333 Low
## 4 6 Female 15 home 14.000000 High
## 5 6 Female 16 home 12.333333 High
## 6 7 Male 16 reputation 12.333333 Highggplot(sacf.dat, aes(x = grades, y = paredu, fill = paredu.cat)) +
geom_point(size = 2, shape = 16, position = position_jitter(0.5)) +
facet_wrap(~ paredu.cat)
nearZeroVar(
cat.dat,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics = TRUE,
names = FALSE,
foreach = FALSE,
allowParallel = TRUE
)## freqRatio percentUnique zeroVar nzv
## Mjob 1.897059 0.7704160 FALSE FALSE
## Fjob 2.027624 0.7704160 FALSE FALSE
## guardian 2.973856 0.4622496 FALSE FALSE
## famsup 1.585657 0.3081664 FALSE FALSE
## activities 1.060317 0.3081664 FALSE FALSE
## romantic 1.715481 0.3081664 FALSE FALSE
## freetime 1.410112 0.7704160 FALSE FALSE
## health 2.008065 0.7704160 FALSE FALSE
## goout 1.413793 0.7704160 FALSE FALSE
## sex 1.439850 0.3081664 FALSE FALSE
## reason 1.912752 0.6163328 FALSE FALSE#None of the variables failed nzv test. Although this is to check for dichotomized variables #we remove Mjob, Fjob, and guardian due to skewness issue.
sac.dat <- select(sacf.dat, sex, age, paredu, famsup, activities, romantic, freetime, health, goout, reason, grades)
head(sac.dat)## sex age paredu famsup activities romantic freetime health goout reason
## 1 Female 18 8 no no no 3 3 4 course
## 2 Female 17 2 yes no no 3 3 3 course
## 3 Female 15 2 no no no 3 3 2 other
## 4 Female 15 6 yes yes yes 2 5 2 home
## 5 Female 16 6 yes no no 3 5 2 home
## 6 Male 16 7 yes yes no 4 5 2 reputation
## grades
## 1 7.333333
## 2 10.333333
## 3 12.333333
## 4 14.000000
## 5 12.333333
## 6 12.333333str(sac.dat)## 'data.frame': 649 obs. of 11 variables:
## $ sex : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 2 2 1 2 2 ...
## $ age : int 18 17 15 15 16 16 16 17 15 15 ...
## $ paredu : int 8 2 2 6 6 7 4 8 5 7 ...
## $ famsup : Factor w/ 2 levels "no","yes": 1 2 1 2 2 2 1 2 2 2 ...
## $ activities: Factor w/ 2 levels "no","yes": 1 1 1 2 1 2 1 1 1 2 ...
## $ romantic : Factor w/ 2 levels "no","yes": 1 1 1 2 1 1 1 1 1 1 ...
## $ freetime : Factor w/ 5 levels "1","2","3","4",..: 3 3 3 2 3 4 4 1 2 5 ...
## $ health : Factor w/ 5 levels "1","2","3","4",..: 3 3 3 5 5 5 3 1 1 5 ...
## $ goout : Factor w/ 5 levels "1","2","3","4",..: 4 3 2 2 2 2 4 4 2 1 ...
## $ reason : Factor w/ 4 levels "home","reputation",..: 3 3 4 1 1 2 1 1 1 1 ...
## $ grades : num 7.33 10.33 12.33 14 12.33 ...summary(sac.dat)## sex age paredu famsup activities romantic
## Female:383 Min. :15.00 Min. :0.000 no :251 no :334 no :410
## Male :266 1st Qu.:16.00 1st Qu.:3.000 yes:398 yes:315 yes:239
## Median :17.00 Median :5.000
## Mean :16.74 Mean :4.821
## 3rd Qu.:18.00 3rd Qu.:6.000
## Max. :22.00 Max. :8.000
## freetime health goout reason grades
## 1: 45 1: 90 1: 48 home :149 Min. : 1.333
## 2:107 2: 78 2:145 reputation:143 1st Qu.:10.000
## 3:251 3:124 3:205 course :285 Median :11.667
## 4:178 4:108 4:141 other : 72 Mean :11.625
## 5: 68 5:249 5:110 3rd Qu.:13.333
## Max. :18.667psych::describe(sac.dat)## vars n mean sd median trimmed mad min max range skew
## sex* 1 649 1.41 0.49 1.00 1.39 0.00 1.00 2.00 1.00 0.37
## age 2 649 16.74 1.22 17.00 16.70 1.48 15.00 22.00 7.00 0.41
## paredu 3 649 4.82 2.03 5.00 4.79 2.97 0.00 8.00 8.00 0.12
## famsup* 4 649 1.61 0.49 2.00 1.64 0.00 1.00 2.00 1.00 -0.46
## activities* 5 649 1.49 0.50 1.00 1.48 0.00 1.00 2.00 1.00 0.06
## romantic* 6 649 1.37 0.48 1.00 1.34 0.00 1.00 2.00 1.00 0.55
## freetime* 7 649 3.18 1.05 3.00 3.19 1.48 1.00 5.00 4.00 -0.18
## health* 8 649 3.54 1.45 4.00 3.67 1.48 1.00 5.00 4.00 -0.50
## goout* 9 649 3.18 1.18 3.00 3.20 1.48 1.00 5.00 4.00 -0.01
## reason* 10 649 2.43 0.96 3.00 2.41 1.48 1.00 4.00 3.00 -0.20
## grades 11 649 11.63 2.83 11.67 11.64 2.47 1.33 18.67 17.33 -0.23
## kurtosis se
## sex* -1.87 0.02
## age 0.05 0.05
## paredu -1.14 0.08
## famsup* -1.79 0.02
## activities* -2.00 0.02
## romantic* -1.71 0.02
## freetime* -0.41 0.04
## health* -1.13 0.06
## goout* -0.87 0.05
## reason* -1.04 0.04
## grades 0.58 0.11sum(is.na(sac.dat))## [1] 0#we kept variables with low skewness but also kept some variables with intermediate skew levels because we are interested in those variables (think they might help predict grades). #checking correlations between variables
car::vif(lm(grades ~ .,
data = sac.dat,
))## GVIF Df GVIF^(1/(2*Df))
## sex 1.129465 1 1.062763
## age 1.101425 1 1.049488
## paredu 1.095749 1 1.046780
## famsup 1.078598 1 1.038556
## activities 1.102624 1 1.050059
## romantic 1.080818 1 1.039624
## freetime 1.433898 4 1.046080
## health 1.142574 4 1.016800
## goout 1.397374 4 1.042711
## reason 1.141863 3 1.022356#correlations between variables and grades are extremely low here. Where 1 means no correlation and larger number means more correlationsset.seed(1234)
train.index <- caret::createDataPartition(sac.dat$grades, p = .7, list = FALSE)
train.dat <- sac.dat[ train.index, ]
test.dat <- sac.dat[ - train.index, ]
dim(train.dat)## [1] 457 11dim(test.dat)## [1] 192 11my.seed <- 111
set.seed(my.seed)
tr.Control <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5
)set.seed(my.seed)
lm.fit <- train(grades ~ .,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
lm.fit$results## intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 TRUE 2.576139 0.1642073 2.015726 0.2196639 0.0861487 0.1693247#random search
set.seed(my.seed)
elastic.fit1 <- train(grades ~ .,
data = train.dat,
method = 'glmnet',
trControl = tr.Control,
verbose = FALSE,
tuneLength = 20,
metric = "RMSE",
preProcess = c("center", "scale")
)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
## There were missing values in resampled performance measures.plot(elastic.fit1)
elastic.fit1$results[ rownames(elastic.fit1$bestTune), ]## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 31 0.1473684 0.2750149 2.560243 0.1659912 1.98834 0.2127696 0.08922157
## MAESD
## 31 0.1535565unique(elastic.fit1$results$alpha) ## [1] 0.1000000 0.1473684 0.1947368 0.2421053 0.2894737 0.3368421 0.3842105
## [8] 0.4315789 0.4789474 0.5263158 0.5736842 0.6210526 0.6684211 0.7157895
## [15] 0.7631579 0.8105263 0.8578947 0.9052632 0.9526316 1.0000000#alpha is close to one end of the tuning range.
unique(elastic.fit1$results$lambda)## [1] 0.0009186832 0.0014244328 0.0022086055 0.0034244777 0.0053097068
## [6] 0.0082327842 0.0127650617 0.0197924297 0.0306884745 0.0475829640
## [11] 0.0737781365 0.1143941649 0.1773699578 0.2750149185 0.4264149708
## [16] 0.6611631410 1.0251438831#grid search
set.seed(my.seed)
elastic.grid <- expand.grid(alpha = seq(0, 0.3, length.out = 20),
lambda = seq(0.1, 0.6, length.out = 20))
set.seed(my.seed)
elastic.fit2 <- train(grades ~ .,
data = train.dat,
method = 'glmnet',
trControl = tr.Control,
verbose = FALSE,
tuneGrid = elastic.grid,
metric = "RMSE",
preProcess = c("center", "scale")
)
plot(elastic.fit2)
elastic.fit2$results[ rownames(elastic.fit2$bestTune), ]## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 130 0.09473684 0.3368421 2.56004 0.1658442 1.988752 0.2142611 0.08915918
## MAESD
## 130 0.1543687unique(elastic.fit2$results$alpha) ## [1] 0.00000000 0.01578947 0.03157895 0.04736842 0.06315789 0.07894737
## [7] 0.09473684 0.11052632 0.12631579 0.14210526 0.15789474 0.17368421
## [13] 0.18947368 0.20526316 0.22105263 0.23684211 0.25263158 0.26842105
## [19] 0.28421053 0.30000000unique(elastic.fit2$results$lambda)## [1] 0.1000000 0.1263158 0.1526316 0.1789474 0.2052632 0.2315789 0.2578947
## [8] 0.2842105 0.3105263 0.3368421 0.3631579 0.3894737 0.4157895 0.4421053
## [15] 0.4684211 0.4947368 0.5210526 0.5473684 0.5736842 0.6000000#alpha and lambda are not at the end of the tuning range now.
# get the upper and lower RMSE bordering the optimal RMSE
opt.RMSE.index <- which( unique(elastic.fit2$results$RMSE) %in%
elastic.fit2$results[rownames(elastic.fit2$bestTune),])
RMSE.new.range <- unique(elastic.fit2$results$RMSE)[c(opt.RMSE.index -1,
opt.RMSE.index,
opt.RMSE.index + 1) ]
elastic.fit2$results[elastic.fit2$results$RMSE %in% RMSE.new.range, ]## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 129 0.09473684 0.3105263 2.560071 0.1657937 1.988831 0.2144791 0.08898615
## 130 0.09473684 0.3368421 2.560040 0.1658442 1.988752 0.2142611 0.08915918
## 131 0.09473684 0.3631579 2.560179 0.1658411 1.988876 0.2140479 0.08930964
## MAESD
## 129 0.1549954
## 130 0.1543687
## 131 0.1538091#how much RMSE varies from one sample to the next = RMSESD/sqrt(50)
cv.sd = elastic.fit2$results [ row.names(elastic.fit2$bestTune),]$RMSESD
cv.n = 50
avCV.SE = cv.sd / sqrt(cv.n)
avCV.SE## [1] 0.03030109#We see that the RMSE has not changed much. The variation in RMSE seems much smaller than the CV RMSE SE = 0.03. So we stop.
#RMSE should vary about 0.03 across different samples. However, the RMSE here vary less than 0.01, so we can stop fine tuning here.#random search
set.seed(my.seed)
tree.fit1 <- train(grades ~ .,
data = train.dat,
trControl = tr.Control,
method = "rpart",
metric = "RMSE",
#preProcess = c("center", "scale"),
tuneLength = 20
)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
## There were missing values in resampled performance measures.tree.fit1$results [ row.names(tree.fit1$bestTune),]## cp RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 18 0.02313252 2.703663 0.073586 2.130176 0.2343079 0.06035126 0.1599496plot(tree.fit1)
rpart.plot(tree.fit1$finalModel)
unique(tree.fit1$results$cp)## [1] 0.003810606 0.004585483 0.005286322 0.005745640 0.006240667 0.006589220
## [7] 0.006784420 0.008405313 0.008519346 0.008640897 0.008660516 0.009572490
## [13] 0.010597562 0.011084748 0.011817110 0.012459137 0.014042632 0.023132523
## [19] 0.031686707 0.070890298#cp is quite close to the extreme end of selected range
#grid search
set.seed(my.seed)
tree.fit2 <- train(grades ~ .,
data = train.dat,
trControl = tr.Control,
method = "rpart",
metric = "RMSE",
#preProcess = c("center", "scale"),
tuneGrid = expand.grid(cp = seq(0.014, 0.032, length.out = 20 ) )
)
tree.fit2$results [ row.names(tree.fit2$bestTune),]## cp RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 10 0.02252632 2.70047 0.07550309 2.128543 0.2361885 0.05958051 0.161072plot(tree.fit2)
rpart.plot(tree.fit2$finalModel)
unique(tree.fit2$results$cp)## [1] 0.01400000 0.01494737 0.01589474 0.01684211 0.01778947 0.01873684
## [7] 0.01968421 0.02063158 0.02157895 0.02252632 0.02347368 0.02442105
## [13] 0.02536842 0.02631579 0.02726316 0.02821053 0.02915789 0.03010526
## [19] 0.03105263 0.03200000#cp is not close to the end of the tuning range, but the number of buckets is too little and we will fine tune that.
split.vec <- c(20, 40, 50, 60, 70, 80)
# create empty dataframe to store results of all minsplit
tree.result.dat <- data.frame()
for(i in split.vec){
set.seed(my.seed)
tree.fit3 <- train(grades ~ .,
data = train.dat,
trControl = tr.Control,
method = "rpart",
metric = "RMSE",
#preProcess = c("center", "scale"),
tuneGrid = expand.grid(cp = seq(0.014, 0.032, length.out = 20 ) ),
control = rpart.control(minsplit = i)
)
temp.result <- tree.fit3$results
temp.result$minsplit = i
tree.result.dat <- rbind(tree.result.dat, temp.result)
}
ggplot(tree.result.dat, aes( x = cp, y = RMSE, color = as.factor(minsplit)) ) +
geom_point() +
geom_line()
tree.result.dat %>%
filter(RMSE == min(RMSE, na.rm = TRUE))## cp RMSE Rsquared MAE RMSESD RsquaredSD MAESD minsplit
## 1 0.02252632 2.70047 0.07550309 2.128543 0.2361885 0.05958051 0.161072 20
## 2 0.02252632 2.70047 0.07550309 2.128543 0.2361885 0.05958051 0.161072 40
## 3 0.02252632 2.70047 0.07550309 2.128543 0.2361885 0.05958051 0.161072 50#we will keep minsplit = 40 and cp = 0.02252632
set.seed(my.seed)
tree.fit4 <- train(grades ~ .,
data = train.dat,
trControl = tr.Control,
method = "rpart",
metric = "RMSE",
#preProcess = c("center", "scale"),
tuneGrid = expand.grid(cp = 0.02252632 ),
control = rpart.control(minsplit = 40)
)
tree.fit4$results [ row.names(tree.fit4$bestTune),]## cp RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.02252632 2.70047 0.07550309 2.128543 0.2361885 0.05958051 0.161072#plot(tree.fit4)
rpart.plot(tree.fit4$finalModel)
set.seed(my.seed)
rf.fit1 <- train(grades ~ .,
data = train.dat,
method = "rf",
trControl = tr.Control ,
#preProc = c("center", "scale"),
ntree = 1000,
metric = "RMSE",
tuneGrid = expand.grid(mtry = seq(1, ncol(train.dat)-1))
)
#node size here = 10# bag.fit
rf.fit1$results## mtry RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2.670301 0.1251016 2.121746 0.2255992 0.07271636 0.1450470
## 2 2 2.633737 0.1138724 2.080641 0.2117456 0.06639839 0.1332613
## 3 3 2.630691 0.1115196 2.071496 0.2019564 0.06008428 0.1289168
## 4 4 2.639827 0.1081964 2.073501 0.1990731 0.05932240 0.1281654
## 5 5 2.646452 0.1068010 2.077764 0.1950594 0.05793634 0.1267241
## 6 6 2.653286 0.1052388 2.079790 0.1924161 0.05631559 0.1279332
## 7 7 2.664393 0.1018892 2.088239 0.1942837 0.05558167 0.1306198
## 8 8 2.668567 0.1018751 2.089174 0.1911831 0.05496624 0.1288483
## 9 9 2.671777 0.1018588 2.092629 0.1915110 0.05566215 0.1304676
## 10 10 2.677769 0.1003134 2.098943 0.1951272 0.05527494 0.1310200rf.fit1$results[ rownames(rf.fit1$bestTune),]## mtry RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 3 3 2.630691 0.1115196 2.071496 0.2019564 0.06008428 0.1289168plot(rf.fit1)
plot.dat <- rf.fit1$results
ggplot(plot.dat, aes(x = mtry, y = Rsquared )) +
geom_point() +
geom_line() +
theme_bw()
#fine tune rf since it looks quite similar to the linear model node.size = c(10, 20, 30, 40, 50, 60)
rf2.results.dat <- NULL
rf.models <- vector("list", length = length(node.size))
for(i in 1: length(node.size) ){
cat("\n Iteration: ",i, " ; Node size = ",
node.size[i], "\n")
set.seed(my.seed)
rf.fit2 <- train(grades ~ .,
data = train.dat,
trControl = tr.Control,
method = "rf",
# preProc = c("center", "scale"),
ntree = 1000,
tuneGrid = expand.grid(mtry = seq(1, ncol(train.dat)-1) ),
nodesize = node.size[i]
)
rf.models[[i]] <- rf.fit2
results.temp <- rf.fit2$results
results.temp$node.size = node.size[i]
# print(results.temp)
rf2.results.dat <- rbind(rf2.results.dat, results.temp)
} ##
## Iteration: 1 ; Node size = 10
##
## Iteration: 2 ; Node size = 20
##
## Iteration: 3 ; Node size = 30
##
## Iteration: 4 ; Node size = 40
##
## Iteration: 5 ; Node size = 50
##
## Iteration: 6 ; Node size = 60rf2plot.dat <- rf2.results.dat
rf2plot.dat$node.size <- factor(rf2plot.dat$node.size)
ggplot(rf2plot.dat, aes(x = mtry, y = RMSE, color = node.size )) +
geom_point() +
geom_line() +
theme_bw()
rf2.results.dat[rf2.results.dat$RMSE == min(rf2.results.dat$RMSE), ]## mtry RMSE Rsquared MAE RMSESD RsquaredSD MAESD node.size
## 57 7 2.587995 0.1456905 2.02236 0.2097109 0.073771 0.1339882 60#from the graph we can see that both node size 10 and 20 does well, almost equally. We will go with node size 10 for our chosen model.
#since there is not a big change from rf.fit1, we will stop fine tuning here.
rf.fitfinal <- rf.models[[ which(node.size == 60) ]]tree.N = c(500, 1000, 2000, 3000)
rf3.results.dat <- NULL
rf.models2 <- vector("list", length = length(tree.N))
for(i in 1:length( tree.N)){
set.seed(30825920)
rf.fit3 <- train(grades ~ .,
data = train.dat,
method = "rf",
trControl = tr.Control ,
preProc = c("center", "scale"),
ntree = tree.N[i],
tuneGrid = expand.grid(mtry = seq(1, ncol(train.dat)-1)
)
)
rf.models2[[i]] <- rf.fit3
results.temp <- rf.fit3$results
results.temp$tree.size = tree.N[i]
rf3.results.dat <- rbind(rf3.results.dat, results.temp)
}rf3plot.dat <- rf3.results.dat
rf3plot.dat$tree.size <- factor(rf3plot.dat$tree.size)
ggplot(rf3plot.dat, aes(x = mtry, y = RMSE, color = tree.size )) +
geom_point() +
geom_line() +
theme_bw()
rf3.results.dat[rf3.results.dat$RMSE == min(rf3.results.dat$RMSE), ]## mtry RMSE Rsquared MAE RMSESD RsquaredSD MAESD tree.size
## 12 2 2.63206 0.1155033 2.081163 0.2654382 0.0878663 0.181373 1000#this did slightly worse than rf.fit2 (otherwise it's the same), so we will use rf.fit2 instead.set.seed(my.seed)
gbm.fit1 <- train(grades ~ .,
data = train.dat,
method = "gbm",
trControl = tr.Control,
preProc = c("center", "scale"),
tuneLength = 20,
verbose = FALSE,
metric = "RMSE"
)plot(gbm.fit1)
gbm.fit1$results[ rownames(gbm.fit1$bestTune),]## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared MAE
## 2 0.1 1 10 100 2.568025 0.161945 1.99818
## RMSESD RsquaredSD MAESD
## 2 0.2098142 0.08911544 0.1500962tree.size <- c(50, 100, 500, 1000, 1500, 2000)
gbm.grid <- expand.grid(interaction.depth = c(1, 2, 3, 4),
n.trees = tree.size,
shrinkage = c(10^-(1:3)),
n.minobsinnode = 10
)
set.seed(my.seed)
gbm.fit2 <- train(grades ~ .,
data = train.dat,
method = "gbm",
trControl = tr.Control,
preProc = c("center", "scale"),
tuneGrid = gbm.grid,
verbose = FALSE,
metric = "RMSE"
)plot(gbm.fit2)
gbm.fit2$results[ rownames(gbm.fit2$bestTune),]## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 28 0.01 1 10 1000 2.563566 0.1640269
## MAE RMSESD RsquaredSD MAESD
## 28 1.993294 0.2114444 0.0891012 0.1492431model.resamples <- resamples( list(Regression = lm.fit,
Elastic = elastic.fit2,
Tree = tree.fit4,
RF = rf.fitfinal,
Boosting = gbm.fit2))
summary(model.resamples)##
## Call:
## summary.resamples(object = model.resamples)
##
## Models: Regression, Elastic, Tree, RF, Boosting
## Number of resamples: 50
##
## MAE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## Regression 1.676993 1.888787 2.033141 2.015726 2.119890 2.364281 0
## Elastic 1.707368 1.869172 1.971440 1.988752 2.089666 2.350665 0
## Tree 1.887932 2.002949 2.094138 2.128543 2.219750 2.589130 0
## RF 1.792896 1.930809 2.012714 2.022360 2.114549 2.363991 0
## Boosting 1.671437 1.883625 1.990164 1.993294 2.066425 2.344192 0
##
## RMSE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## Regression 2.138816 2.453151 2.560630 2.576139 2.704602 3.163428 0
## Elastic 2.170629 2.431644 2.537092 2.560040 2.686954 3.188372 0
## Tree 2.337741 2.524175 2.634610 2.700470 2.851354 3.305859 0
## RF 2.264037 2.461165 2.567661 2.587995 2.722219 3.191767 0
## Boosting 2.188182 2.437061 2.529624 2.563566 2.688840 3.183981 0
##
## Rsquared
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## Regression 1.604298e-03 0.09878338 0.15554560 0.16420725 0.2294914 0.3419734
## Elastic 1.862394e-02 0.09878126 0.15069644 0.16584422 0.2180598 0.3544555
## Tree 3.409078e-05 0.02727492 0.06552866 0.07550309 0.1122245 0.2273834
## RF 2.582424e-02 0.08248527 0.13694023 0.14569050 0.1864163 0.3210509
## Boosting 1.360126e-02 0.09399700 0.14955917 0.16402686 0.2181890 0.3773888
## NA's
## Regression 0
## Elastic 0
## Tree 0
## RF 0
## Boosting 0dotplot(model.resamples, metric = "RMSE")
splom(model.resamples, metric = "RMSE")
summary(diff(model.resamples))##
## Call:
## summary.diff.resamples(object = diff(model.resamples))
##
## p-value adjustment: bonferroni
## Upper diagonal: estimates of the difference
## Lower diagonal: p-value for H0: difference = 0
##
## MAE
## Regression Elastic Tree RF Boosting
## Regression 0.026974 -0.112817 -0.006633 0.022432
## Elastic 0.00413 -0.139792 -0.033608 -0.004542
## Tree 3.053e-06 4.916e-11 0.106184 0.135249
## RF 1.00000 0.03498 2.258e-10 0.029065
## Boosting 0.30780 1.00000 2.249e-10 0.06324
##
## RMSE
## Regression Elastic Tree RF Boosting
## Regression 0.016100 -0.124330 -0.011855 0.012574
## Elastic 0.47657 -0.140430 -0.027955 -0.003526
## Tree 6.772e-06 1.079e-09 0.112475 0.136904
## RF 1.00000 0.05636 3.946e-10 0.024429
## Boosting 1.00000 1.00000 5.221e-09 0.28004
##
## Rsquared
## Regression Elastic Tree RF Boosting
## Regression -0.0016370 0.0887042 0.0185168 0.0001804
## Elastic 1.000000 0.0903411 0.0201537 0.0018174
## Tree 6.710e-10 2.591e-10 -0.0701874 -0.0885238
## RF 0.108280 0.013471 4.844e-10 -0.0183364
## Boosting 1.000000 1.000000 8.889e-11 0.008829RMSE.dat <- model.resamples$values %>%
select(Resample, ends_with("~RMSE"))
names(RMSE.dat) <- c("folds", "Logistic", "Elastic", "Tree", "RandomForest","GBM")
RMSE.dat %>%
pivot_longer(cols = !folds,
names_to = "Method",
values_to = "RMSE") %>%
ggplot(aes(x = Method, y = RMSE, color = Method)) +
geom_point(position = position_jitter(width = 0.1))
RMSE.long <- RMSE.dat %>%
pivot_longer(cols = !folds,
names_to = "Method",
values_to = "RMSE") %>%
separate(folds, into = c("Fold", "Rep"))
RMSE.long %>%
ggplot(aes(x = Fold, y = RMSE, color = Method)) +
geom_point(position = position_dodge(width = 0.3), alpha = 0.3) +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
geom = "pointrange", position = position_dodge(width = 0.5)) +
theme_bw()
RMSE.long %>%
ggplot(aes(x = Method, y = RMSE, color = Rep)) +
geom_point(position = position_dodge(width = 0.3), alpha = 0.3) +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
geom = "pointrange", position = position_dodge(width = 0.5)) +
theme_bw()
# getting cv measure for best tune :
cv.dat <- rbind(lm.fit$results[rownames(lm.fit$bestTune), c("RMSE", "RMSESD") ],
elastic.fit2$results[rownames(elastic.fit2$bestTune), c("RMSE", "RMSESD") ],
tree.fit4$results[rownames(tree.fit4$bestTune), c("RMSE", "RMSESD") ],
rf.fitfinal$results[rownames(rf.fitfinal$bestTune), c("RMSE", "RMSESD")],
gbm.fit2$results[rownames(gbm.fit2$bestTune), c("RMSE", "RMSESD")]
)
# getting model names from resamples()
cv.dat$model <- model.resamples$models
#the number of repeated cv folds = 10 x 5 = 50
cv.N = 50
#SE = sd / sqrt(length(x))
#table:
cv.dat %>%
mutate(SE = RMSESD / sqrt(cv.N))## RMSE RMSESD model SE
## 1 2.576139 0.2196639 Regression 0.03106517
## 130 2.560040 0.2142611 Elastic 0.03030109
## 11 2.700470 0.2361885 Tree 0.03340210
## 7 2.587995 0.2097109 RF 0.02965759
## 28 2.563566 0.2114444 Boosting 0.02990275# plot:
cv.dat %>%
mutate(SE = RMSESD / sqrt(cv.N)) %>%
ggplot(aes(x = model, y = RMSE, color = model)) +
# 95%CI for CV average RMSE
geom_pointrange(aes(ymin = RMSE - 1.96*SE, ymax = RMSE + 1.96*SE) )
p1 <- vip(lm.fit) + ggtitle("Regression")
p2 <- vip(elastic.fit2) + ggtitle("Elastic")
p3 <- vip(tree.fit4) + ggtitle("Tree")
p4 <- vip(rf.fitfinal) + ggtitle("RF")
p5 <- vip(gbm.fit2) + ggtitle("Boosting")
grid.arrange(p1, p2, p3, p4, p5, ncol = 5)
#combined top 3 from all:
##paredu
##sex
#goout
##reason
##age#Getting RMSE and Rsquared for each model on test.dat
#linear regression
lm.pred <- predict(lm.fit, newdata = test.dat, type = "raw")
lm.RMSE.pred <- RMSE(lm.pred, test.dat$grades)
lm.R2.pred <- R2(lm.pred, test.dat$grades)
#elastic net
elastic.pred <- predict(elastic.fit2, newdata = test.dat, type = "raw")
elastic.RMSE.pred <- RMSE(elastic.pred, test.dat$grades)
elastic.R2.pred <- R2(elastic.pred, test.dat$grades)
#decision tree
tree.pred <- predict(tree.fit4, newdata = test.dat, type = "raw")
tree.RMSE.pred <- RMSE(tree.pred, test.dat$grades)
tree.R2.pred <- R2(tree.pred, test.dat$grades)
#random forest
rf.pred <- predict(rf.fitfinal, newdata = test.dat, type = "raw")
rf.RMSE.pred <- RMSE(rf.pred, test.dat$grades)
rf.R2.pred <- R2(rf.pred, test.dat$grades)
#boosting
gbm.pred <- predict(gbm.fit2, newdata = test.dat, type = "raw")
gbm.RMSE.pred <- RMSE(gbm.pred, test.dat$grades)
gbm.R2.pred <- R2(gbm.pred, test.dat$grades)
test.results<- data.frame(models=c("lm","elastic","tree","rf","gbm"),
train.RMSE=c(lm.fit$results$RMSE,
elastic.fit2$results[ rownames(elastic.fit2$bestTune), ]$RMSE,
tree.fit4$results [ row.names(tree.fit4$bestTune),]$RMSE,
rf2.results.dat[rf2.results.dat$RMSE == min(rf2.results.dat$RMSE), ]$RMSE,
gbm.fit2$results[ rownames(gbm.fit2$bestTune),]$RMSE),
train.R2=c(lm.fit$results$Rsquared,
elastic.fit2$results[ rownames(elastic.fit2$bestTune), ]$Rsquared,
tree.fit4$results [ row.names(tree.fit4$bestTune),]$Rsquared,
rf2.results.dat[rf2.results.dat$RMSE == min(rf2.results.dat$RMSE), ]$Rsquared,
gbm.fit2$results[ rownames(gbm.fit2$bestTune),]$Rsquared
),
test.RMSE=c(lm.RMSE.pred,elastic.RMSE.pred,tree.RMSE.pred,rf.RMSE.pred,gbm.RMSE.pred),
test.R2=c(lm.R2.pred,elastic.R2.pred,tree.R2.pred,rf.R2.pred,gbm.R2.pred))
print(test.results)## models train.RMSE train.R2 test.RMSE test.R2
## 1 lm 2.576139 0.16420725 2.782950 0.12181139
## 2 elastic 2.560040 0.16584422 2.758887 0.12428297
## 3 tree 2.700470 0.07550309 2.831194 0.08049928
## 4 rf 2.587995 0.14569050 2.758322 0.13174238
## 5 gbm 2.563566 0.16402686 2.757155 0.12735992#minimum RMSE for train set
test.results[which.min(test.results$train.RMSE),]## models train.RMSE train.R2 test.RMSE test.R2
## 2 elastic 2.56004 0.1658442 2.758887 0.124283#minimum RMSE for test set
test.results[which.min(test.results$test.RMSE),]## models train.RMSE train.R2 test.RMSE test.R2
## 5 gbm 2.563566 0.1640269 2.757155 0.1273599#based on the results, we can see that elastic had the lowest RMSE and highest Rsquared in train data. However, gbm had the lowest RMSE and higher Rsquared in test data. boot.iter = 1000
set.seed(my.seed)
boot.dat <- data.frame(LM= vector(length = boot.iter),
Elastic = vector(length = boot.iter),
Tree = vector(length = boot.iter),
RF = vector(length = boot.iter),
GBM = vector(length = boot.iter)
)
for ( i in 1: boot.iter){
# get a bootstrap sample:
boot.index <- sample( nrow(test.dat), replace = TRUE )
boot.sample <- test.dat[ boot.index, ]
pred.dat <- data.frame( y.lm = predict(lm.fit, newdata = boot.sample),
y.elastic = predict(elastic.fit2, newdata = boot.sample),
y.tree= predict(tree.fit4, newdata = boot.sample),
y.rf = predict(rf.fitfinal, newdata = boot.sample),
y.gbm = predict(gbm.fit2, newdata = boot.sample))
sq.err.dat <- (boot.sample$grades - pred.dat)^2
boot.MSE = colMeans(sq.err.dat)
boot.RMSE = sqrt(boot.MSE)
boot.dat[i, ] <- boot.RMSE
}
head(boot.dat)## LM Elastic Tree RF GBM
## 1 2.665443 2.606571 2.611300 2.574718 2.602244
## 2 2.848095 2.782658 2.879957 2.739207 2.793967
## 3 2.798956 2.758910 2.728206 2.739180 2.697285
## 4 2.704563 2.656495 2.707461 2.598371 2.652230
## 5 2.452856 2.426535 2.568996 2.464149 2.463650
## 6 2.626322 2.587371 2.629527 2.550545 2.591210mean.dat <- boot.dat %>%
pivot_longer(cols = everything(),
names_to = "Model",
values_to = "RMSE") %>%
group_by(Model) %>%
summarize(mean = mean(RMSE))
mean.dat## # A tibble: 5 × 2
## Model mean
## <chr> <dbl>
## 1 Elastic 2.74
## 2 GBM 2.74
## 3 LM 2.76
## 4 RF 2.74
## 5 Tree 2.82boot.dat %>%
pivot_longer(cols = everything(),
names_to = "Model",
values_to = "boot.RMSE") %>%
ggplot(aes(x = boot.RMSE, fill = Model)) +
geom_histogram(col = "grey") +
geom_vline(data = mean.dat, aes(xintercept = mean), col = "black", lty = 2 ) +
facet_wrap( ~ Model, ncol = 1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# parametric bootstrap 95%CI: assumes normal distribution:
par.ci.dat <- data.frame(RMSE =apply(boot.dat, 2, mean, na.rm = TRUE),
SD = apply(boot.dat, 2, sd, na.rm = TRUE),
error.type = "Test-bootstrap",
model.type = c("LM","Elastic", "Tree","RF","GBM")
) %>%
group_by(model.type) %>%
summarize( RMSE = RMSE,
lower = RMSE - 1.96*SD,
upper = RMSE + 1.96*SD,
error.type = "Test.boot.param" )
# empirical 95%CI: percentile bootstrap CI
plyr::ldply(boot.dat, quantile, c(.025, .975))## .id 2.5% 97.5%
## 1 LM 2.422336 3.105772
## 2 Elastic 2.379332 3.096791
## 3 Tree 2.473934 3.170690
## 4 RF 2.378803 3.109788
## 5 GBM 2.385917 3.098516par.ci.dat %>%
ggplot(aes(x = model.type, y = RMSE, color = model.type)) +
geom_pointrange(aes(ymin = lower,
ymax = upper),
position = position_dodge(width = 0.2))
test.ci.dat <- data.frame(RMSE = test.results$test.RMSE,
SD = apply(boot.dat, 2, sd, na.rm = TRUE),
error.type = "Test-bootstrap",
model.type = c("LM","Elastic", "Tree","RF","GBM")
) %>%
group_by(model.type) %>%
summarize( RMSE = RMSE,
lower = RMSE - 1.96*SD,
upper = RMSE + 1.96*SD,
error.type = "Test.boot.param" )
test.ci.dat %>%
ggplot(aes(x = model.type, y = RMSE, color = model.type)) +
geom_pointrange(aes(ymin = lower,
ymax = upper),
position = position_dodge(width = 0.2))
print(par.ci.dat)## # A tibble: 5 × 5
## model.type RMSE lower upper error.type
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Elastic 2.74 2.38 3.10 Test.boot.param
## 2 GBM 2.74 2.38 3.10 Test.boot.param
## 3 LM 2.76 2.43 3.10 Test.boot.param
## 4 RF 2.74 2.38 3.11 Test.boot.param
## 5 Tree 2.82 2.46 3.17 Test.boot.paramprint(test.ci.dat)## # A tibble: 5 × 5
## model.type RMSE lower upper error.type
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Elastic 2.76 2.40 3.12 Test.boot.param
## 2 GBM 2.76 2.40 3.12 Test.boot.param
## 3 LM 2.78 2.44 3.12 Test.boot.param
## 4 RF 2.76 2.39 3.12 Test.boot.param
## 5 Tree 2.83 2.48 3.18 Test.boot.param#combined top 3 from all:
##paredu
##sex
#goout
##reason
##age
#first we try paredu, age, sex
pdp.lm1 <- partial(lm.fit, pred.var = c("paredu", "age", "sex"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.elastic1 <- partial(elastic.fit2, pred.var = c("paredu", "age", "sex"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.tree1 <- partial(tree.fit4, pred.var = c("paredu", "age", "sex"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.rf1 <- partial(rf.fitfinal, pred.var = c("paredu", "age", "sex"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.gbm1 <- partial(gbm.fit2, pred.var = c("paredu", "age", "sex"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
lm.plot1 <- plotPartial(pdp.lm1, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Linear Regression"
)
elastic.plot1 <- plotPartial(pdp.elastic1, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Regularised Regression"
)
tree.plot1 <- plotPartial(pdp.tree1, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Decision Tree"
)
rf.plot1 <- plotPartial(pdp.rf1, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Random Forest"
)
gbm.plot1 <- plotPartial(pdp.gbm1, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Gradient Boosting"
)
grid.arrange(lm.plot1, elastic.plot1, tree.plot1, rf.plot1, gbm.plot1, ncol = 2, nrow = 3)
#now we try paredu, age, reason
pdp.lm2 <- partial(lm.fit, pred.var = c("paredu", "age", "reason"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.elastic2 <- partial(elastic.fit2, pred.var = c("paredu", "age", "reason"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.tree2 <- partial(tree.fit4, pred.var = c("paredu", "age", "reason"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.rf2 <- partial(rf.fitfinal, pred.var = c("paredu", "age", "reason"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.gbm2 <- partial(gbm.fit2, pred.var = c("paredu", "age", "reason"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
lm.plot2 <- plotPartial(pdp.lm2, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Linear Regression"
)
elastic.plot2 <- plotPartial(pdp.elastic2, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Regularised Regression"
)
tree.plot2 <- plotPartial(pdp.tree2, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Decision Tree"
)
rf.plot2 <- plotPartial(pdp.rf2, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Random Forest"
)
gbm.plot2 <- plotPartial(pdp.gbm2, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Gradient Boosting"
)
grid.arrange(lm.plot2, elastic.plot2, tree.plot2, rf.plot2, gbm.plot2, ncol = 2, nrow = 3)
#there are hints of interaction across reason in decision trees#lastly we try paredu, age, goout
pdp.lm3 <- partial(lm.fit, pred.var = c("paredu", "age", "goout"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.elastic3 <- partial(elastic.fit2, pred.var = c("paredu", "age", "goout"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.tree3 <- partial(tree.fit4, pred.var = c("paredu", "age", "goout"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.rf3 <- partial(rf.fitfinal, pred.var = c("paredu", "age", "goout"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
pdp.gbm3 <- partial(gbm.fit2, pred.var = c("paredu", "age", "goout"),
plot.engine = "ggplot",
train = train.dat,
chull = FALSE)
lm.plot3 <- plotPartial(pdp.lm3, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Linear Regression"
)
elastic.plot3 <- plotPartial(pdp.elastic3, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Regularised Regression"
)
tree.plot3 <- plotPartial(pdp.tree3, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Decision Tree"
)
rf.plot3 <- plotPartial(pdp.rf3, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Random Forest"
)
gbm.plot3 <- plotPartial(pdp.gbm3, levelplot = FALSE, zlab = "grades", drape = TRUE,
colorkey = TRUE,
# screen = list(z = -20, x = -80),
main = "Gradient Boosting"
)
grid.arrange(lm.plot3, elastic.plot3, tree.plot3, rf.plot3, gbm.plot3, ncol = 2, nrow = 3)
ggplot(pdp.tree2, aes(x = paredu, y = yhat, color = age)) +
geom_line(aes(group = as.factor(age) ) ) +
facet_wrap(~reason)
ggplot(pdp.gbm1, aes(x = paredu, y = yhat, color = age)) +
geom_line(aes(group = as.factor(age) ) ) +
facet_wrap(~sex)
set.seed(my.seed)
lm.add1 <- train(grades ~ . + I(paredu^2),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.add2 <- train(grades ~ . + I(paredu^2) + I(paredu^3),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.add3 <- train(grades ~ . + I(paredu^2) + I(paredu^3) + I(paredu^4),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.add4 <- train(grades ~ . + I(age^2),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.add5 <- train(grades ~ . + I(age^2) + I(age^3),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.add6 <- train(grades ~ . + I(age^2) + I(age^3) + I(age^4),
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
lm.addresults <- rbind(lm.fit$results, lm.add1$results, lm.add2$results, lm.add3$results, lm.add4$results, lm.add5$results, lm.add6$results)
print(lm.addresults)## intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 TRUE 2.576139 0.1642073 2.015726 0.2196639 0.08614870 0.1693247
## 2 TRUE 2.578394 0.1632450 2.015453 0.2195351 0.08587161 0.1702570
## 3 TRUE 2.580669 0.1619890 2.017806 0.2196297 0.08588693 0.1706715
## 4 TRUE 2.584801 0.1598574 2.022239 0.2189800 0.08527003 0.1700173
## 5 TRUE 2.569060 0.1696336 2.009235 0.2213509 0.08806187 0.1689818
## 6 TRUE 2.576036 0.1662082 2.017741 0.2200569 0.08735137 0.1697163
## 7 TRUE 2.656088 0.1442942 2.049506 0.2957550 0.09242126 0.1792705lm.addresults[which.min(lm.addresults$RMSE),]## intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 5 TRUE 2.56906 0.1696336 2.009235 0.2213509 0.08806187 0.1689818#we see that lm.add4, with addititive effect of age helped reduce RMSE by a little and increase Rsquared by a little as well. Overall, not a lot of effect. #no interactions were shown in PDPs but we will try anyway.
set.seed(my.seed)
lm.int1 <- train(grades ~ . + age:reason,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int2 <- train(grades ~ . + I(age^2) + age:reason,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int3 <- train(grades ~ . + I(age^2) + I(age^3) + age:reason,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int4 <- train(grades ~ . + age:sex,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int5 <- train(grades ~ . + I(age^2) + age:sex,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int6 <- train(grades ~ . + I(age^2) + I(age^3) + age:sex,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int7 <- train(grades ~ . + age:paredu,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int8 <- train(grades ~ . + I(age^2) + age:paredu,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int9 <- train(grades ~ . + I(age^2) + I(age^3) + age:paredu,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int10 <- train(grades ~ . + age:goout,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int11 <- train(grades ~ . + I(age^2) + age:goout,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
set.seed(my.seed)
lm.int12 <- train(grades ~ . + I(age^2) + I(age^3) + age:goout,
method = "lm",
trControl = tr.Control,
data = train.dat,
metric = "RMSE",
preProcess = c("center", "scale")
)
lm.intresults <- rbind(lm.fit$results, lm.int1$results, lm.int2$results, lm.int3$results, lm.int4$results, lm.int5$results, lm.int6$results, lm.int7$results, lm.int8$results, lm.int9$results, lm.int10$results, lm.int11$results, lm.int12$results )
print(lm.intresults)## intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 TRUE 2.576139 0.1642073 2.015726 0.2196639 0.08614870 0.1693247
## 2 TRUE 2.578179 0.1642603 2.025673 0.2102657 0.08328293 0.1573871
## 3 TRUE 2.574482 0.1685998 2.015499 0.2140838 0.08557726 0.1625302
## 4 TRUE 2.586206 0.1620217 2.025086 0.2117536 0.08447715 0.1632040
## 5 TRUE 2.576998 0.1645093 2.023278 0.2229984 0.08769018 0.1743435
## 6 TRUE 2.571421 0.1690507 2.014922 0.2244917 0.08889643 0.1739584
## 7 TRUE 2.577460 0.1662290 2.022782 0.2222795 0.08856331 0.1741426
## 8 TRUE 2.577878 0.1637266 2.017023 0.2206085 0.08699967 0.1676820
## 9 TRUE 2.574063 0.1672966 2.012224 0.2216906 0.08824220 0.1681112
## 10 TRUE 2.582554 0.1630163 2.021803 0.2197465 0.08695132 0.1692731
## 11 TRUE 2.586052 0.1605505 2.023655 0.2165140 0.08405205 0.1644683
## 12 TRUE 2.580254 0.1652532 2.017811 0.2186990 0.08456856 0.1608782
## 13 TRUE 2.593660 0.1583121 2.030608 0.2169911 0.08325163 0.1635244lm.intresults[which.min(lm.intresults$RMSE),]## intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 6 TRUE 2.571421 0.1690507 2.014922 0.2244917 0.08889643 0.1739584#we see that lm.int5 with additive age and interaction between age and sex helped decrease RMSE and increase Rsquared by a little, but still not so much. This is also higher than the RMSE of the additive model from before.
#since the improvement is minimal, we will not use it for further analyses. # get predictions
res.dat <- data.frame(test.y = test.dat$grades,
lm.pred,
elastic.pred,
tree.pred,
rf.pred,
gbm.pred)
# get residuals
res.dat <- res.dat %>%
mutate(
resid.lm = test.y - lm.pred,
resid.elastic = test.y - elastic.pred,
resid.tree= test.y - tree.pred,
resid.rf = test.y - rf.pred,
resid.gbm = test.y - gbm.pred
)#transform to long format
#first get a residuals only dataset, then merge with a predictions only dataset:
# 1. get residuals only data:
residuals.long <- res.dat %>%
select(resid.lm, resid.elastic, resid.tree, resid.rf, resid.gbm, test.y) %>%
pivot_longer(cols = !test.y,
names_to = "method",
values_to = "residuals")
#clean up the labels in "method"
residuals.long <-
residuals.long %>%
mutate(method = case_when(method == "resid.lm" ~ "Linear",
method == "resid.elastic" ~ "Elastic",
method == "resid.tree" ~ "Tree",
method == "resid.rf" ~ "RF",
method == "resid.gbm" ~ "GBM")
)%>%
mutate(ID = 1:nrow(residuals.long))
# 2.get predictions-only data:
predictions.long <- res.dat %>%
select(lm.pred, elastic.pred, tree.pred, rf.pred,gbm.pred,test.y) %>%
pivot_longer(cols = !test.y,
names_to = "method",
values_to = "predictions")
#clean up the labels in "method"
predictions.long <-
predictions.long %>%
mutate(method = case_when(method == "lm.pred" ~ "Linear",
method == "elastic.pred" ~ "Elastic",
method == "tree.pred" ~ "Tree",
method == "rf.pred" ~ "RF",
method == "gbm.pred" ~ "GBM")
) %>%
mutate(ID = 1:nrow(predictions.long))
# 3. merge both to get the diagnostics dataset:
diag.dat <- merge(predictions.long, residuals.long)
head(diag.dat)## test.y method ID predictions residuals
## 1 1.666667 Elastic 867 12.631518 -10.964852
## 2 1.666667 Elastic 877 9.590935 -7.924269
## 3 1.666667 GBM 870 13.007687 -11.341020
## 4 1.666667 GBM 880 8.566205 -6.899539
## 5 1.666667 Linear 866 12.043743 -10.377076
## 6 1.666667 Linear 876 9.493757 -7.827090ggplot(diag.dat, aes(x = predictions, y = residuals)) +
geom_point(alpha = 0.2) +
geom_hline(yintercept = 0, col = "red", lty = 2) +
geom_smooth(se = FALSE, lty = 1) +
facet_wrap(~method) +
theme_minimal()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 12.622## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.6218## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.4096e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 2.5722
ggplot(diag.dat, aes(x = test.y, y = predictions)) +
geom_point(alpha = 0.2) +
geom_abline(intercept = 0, slope = 1, col = "red", lty = 2) +
geom_smooth(se = FALSE, lty = 1) +
facet_wrap(~method) +
theme_minimal()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
dv = "grades"
pred.vars <- names(sac.dat)[!names(sac.dat)%in% dv]
explainer.dat <- test.dat
# build explainer for linear regression:
lm.exp <- DALEX::explain(model = lm.fit,
data = explainer.dat[ , pred.vars],
y = explainer.dat[ , dv],
label = "Linear Regression",
type = "regression")## Preparation of a new explainer is initiated
## -> model label : Linear Regression
## -> data : 192 rows 10 cols
## -> target variable : 192 values
## -> predict function : yhat.train will be used ( default )
## -> predicted values : No value for predict function target column. ( default )
## -> model_info : package caret , ver. 6.0.93 , task regression ( default )
## -> model_info : type set to regression
## -> predicted values : numerical, min = 8.445585 , mean = 11.64295 , max = 15.1418
## -> residual function : difference between y and yhat ( default )
## -> residuals : numerical, min = -10.37708 , mean = -0.1099685 , max = 7.464469
## A new explainer has been created!# build explainer for elastic net:
elastic.exp <- DALEX::explain(model = elastic.fit2,
data = explainer.dat[ , pred.vars],
y = explainer.dat[, dv],
label = "Elastic",
type = "regression")## Preparation of a new explainer is initiated
## -> model label : Elastic
## -> data : 192 rows 10 cols
## -> target variable : 192 values
## -> predict function : yhat.train will be used ( default )
## -> predicted values : No value for predict function target column. ( default )
## -> model_info : package caret , ver. 6.0.93 , task regression ( default )
## -> model_info : type set to regression
## -> predicted values : numerical, min = 9.225666 , mean = 11.65599 , max = 14.47265
## -> residual function : difference between y and yhat ( default )
## -> residuals : numerical, min = -10.96485 , mean = -0.1230071 , max = 7.016845
## A new explainer has been created!# build explainer for tree:
tree.exp <- DALEX::explain(model = tree.fit4,
data = explainer.dat[ , pred.vars],
y = explainer.dat[, dv],
label = "Tree",
type = "regression")## Preparation of a new explainer is initiated
## -> model label : Tree
## -> data : 192 rows 10 cols
## -> target variable : 192 values
## -> predict function : yhat.train will be used ( default )
## -> predicted values : No value for predict function target column. ( default )
## -> model_info : package caret , ver. 6.0.93 , task regression ( default )
## -> model_info : type set to regression
## -> predicted values : numerical, min = 9.013889 , mean = 11.70908 , max = 12.60381
## -> residual function : difference between y and yhat ( default )
## -> residuals : numerical, min = -10.93714 , mean = -0.1760946 , max = 6
## A new explainer has been created!# build explainer for RF:
rf.exp <- DALEX::explain(model = rf.fitfinal,
data = explainer.dat[ , pred.vars],
y = explainer.dat[, dv],
label = "Random Forest",
type = "regression")## Preparation of a new explainer is initiated
## -> model label : Random Forest
## -> data : 192 rows 10 cols
## -> target variable : 192 values
## -> predict function : yhat.train will be used ( default )
## -> predicted values : No value for predict function target column. ( default )
## -> model_info : package caret , ver. 6.0.93 , task regression ( default )
## -> model_info : type set to regression
## -> predicted values : numerical, min = 9.453839 , mean = 11.67044 , max = 13.24907
## -> residual function : difference between y and yhat ( default )
## -> residuals : numerical, min = -11.15196 , mean = -0.1374501 , max = 5.935747
## A new explainer has been created!# build explainer for GBM:
gbm.exp <- DALEX::explain(model = gbm.fit2,
data = explainer.dat[ , pred.vars],
y= explainer.dat[, dv],
label = "GBM",
type = "regression")## Preparation of a new explainer is initiated
## -> model label : GBM
## -> data : 192 rows 10 cols
## -> target variable : 192 values
## -> predict function : yhat.train will be used ( default )
## -> predicted values : No value for predict function target column. ( default )
## -> model_info : package caret , ver. 6.0.93 , task regression ( default )
## -> model_info : type set to regression
## -> predicted values : numerical, min = 8.256733 , mean = 11.62835 , max = 14.57703
## -> residual function : difference between y and yhat ( default )
## -> residuals : numerical, min = -11.34102 , mean = -0.09536118 , max = 7.176075
## A new explainer has been created!set.seed(my.seed)
vip_lm <- model_parts(explainer = lm.exp, B = 50, N = NULL)
set.seed(my.seed)
vip_elastic <- model_parts(explainer = elastic.exp, B = 50, N = NULL)
set.seed(my.seed)
vip_tree <- model_parts(explainer = tree.exp, B = 50, N = NULL)
set.seed(my.seed)
vip_rf <- model_parts(explainer = rf.exp, B = 50, N = NULL)
set.seed(my.seed)
vip_gbm <- model_parts(explainer = gbm.exp, B = 50, N = NULL)plot(vip_lm, vip_elastic,vip_tree, vip_rf, vip_gbm) +
ggtitle("Mean variable-importance over 50 permutations", "") 
pdp_lm.age <- model_profile(explainer = lm.exp, variables = "age")
pdp_elastic.age <- model_profile(explainer = elastic.exp, variables = "age")
pdp_tree.age <- model_profile(explainer = tree.exp, variables = "age")
pdp_rf.age <- model_profile(explainer = rf.exp, variables = "age")
pdp_gbm.age <- model_profile(explainer = gbm.exp, variables = "age")
plot(pdp_lm.age, pdp_elastic.age, pdp_tree.age, pdp_rf.age, pdp_gbm.age) +
ggtitle("Partial-dependence profile for age") 
pdp_lm.paredu <- model_profile(explainer = lm.exp, variables = "paredu")
pdp_elastic.paredu <- model_profile(explainer = elastic.exp, variables = "paredu")
pdp_tree.paredu <- model_profile(explainer = tree.exp, variables = "paredu")
pdp_rf.paredu <- model_profile(explainer = rf.exp, variables = "paredu")
pdp_gbm.paredu <- model_profile(explainer = gbm.exp, variables = "paredu")
plot(pdp_lm.paredu, pdp_elastic.paredu, pdp_tree.paredu, pdp_rf.paredu, pdp_gbm.paredu) +
ggtitle("Partial-dependence profile for paredu") 
plot(pdp_lm.paredu, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for paredu (lm)")
plot(pdp_elastic.paredu, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for paredu (elastic)")
plot(pdp_tree.paredu, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for paredu (tree)")
plot(pdp_rf.paredu, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for paredu (rf)") 
plot(pdp_gbm.paredu, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for paredu (gbm)") 
plot(pdp_lm.age, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for age (lm)") 
plot(pdp_elastic.age, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for age (elastic)")
plot(pdp_tree.age, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for age (tree)") 
plot(pdp_rf.age, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for age (rf)") 
plot(pdp_gbm.age, geom = "profiles") +
ggtitle("Ceteris-paribus and partial-dependence profiles for age (gbm)")
nesh.dat <- data.frame(
sex = "Male",
age = 17,
paredu = 7,
famsup = "yes",
activities = "no",
romantic = "yes",
freetime = "1",
health = "4",
goout = "1",
reason = "home"
)
predict(lm.fit, newdata = nesh.dat)## 1
## 10.19554predict(elastic.fit2, newdata = nesh.dat)## [1] 11.24229predict(tree.fit4, newdata = nesh.dat)## 1
## 12.60381predict(rf.fitfinal, newdata = nesh.dat)## 1
## 12.0188predict(gbm.fit2, newdata = nesh.dat)## [1] 11.74516cp.lm <- predict_profile(explainer = lm.exp,
new_observation = nesh.dat)
cp.elastic <- predict_profile(explainer = elastic.exp,
new_observation = nesh.dat)
cp.tree <- predict_profile(explainer = tree.exp,
new_observation = nesh.dat)
cp.rf <- predict_profile(explainer = rf.exp,
new_observation = nesh.dat)
cp.gbm <- predict_profile(explainer = gbm.exp,
new_observation = nesh.dat)
cp.lm## Top profiles :
## sex age paredu famsup activities romantic freetime health goout
## 1 Female 17.00 7 yes no yes 1 4 1
## 1.1 Male 17.00 7 yes no yes 1 4 1
## 11 Male 15.00 7 yes no yes 1 4 1
## 1.110 Male 15.06 7 yes no yes 1 4 1
## 1.2 Male 15.12 7 yes no yes 1 4 1
## 1.3 Male 15.18 7 yes no yes 1 4 1
## reason _yhat_ _vname_ _ids_ _label_
## 1 home 11.38620 sex 1 Linear Regression
## 1.1 home 10.19554 sex 1 Linear Regression
## 11 home 10.62430 age 1 Linear Regression
## 1.110 home 10.61144 age 1 Linear Regression
## 1.2 home 10.59858 age 1 Linear Regression
## 1.3 home 10.58571 age 1 Linear Regression
##
##
## Top observations:
## sex age paredu famsup activities romantic freetime health goout reason
## 1 Male 17 7 yes no yes 1 4 1 home
## _yhat_ _label_ _ids_
## 1 10.19554 Linear Regression 1cp.elastic## Top profiles :
## sex age paredu famsup activities romantic freetime health goout
## 1 Female 17.00 7 yes no yes 1 4 1
## 1.1 Male 17.00 7 yes no yes 1 4 1
## 11 Male 15.00 7 yes no yes 1 4 1
## 1.110 Male 15.06 7 yes no yes 1 4 1
## 1.2 Male 15.12 7 yes no yes 1 4 1
## 1.3 Male 15.18 7 yes no yes 1 4 1
## reason _yhat_ _vname_ _ids_ _label_
## 1 home 12.14735 sex 1 Elastic
## 1.1 home 11.24229 sex 1 Elastic
## 11 home 11.58860 age 1 Elastic
## 1.110 home 11.57821 age 1 Elastic
## 1.2 home 11.56782 age 1 Elastic
## 1.3 home 11.55743 age 1 Elastic
##
##
## Top observations:
## sex age paredu famsup activities romantic freetime health goout reason
## 1 Male 17 7 yes no yes 1 4 1 home
## _yhat_ _label_ _ids_
## 1 11.24229 Elastic 1cp.tree## Top profiles :
## sex age paredu famsup activities romantic freetime health goout
## 1 Female 17.00 7 yes no yes 1 4 1
## 1.1 Male 17.00 7 yes no yes 1 4 1
## 11 Male 15.00 7 yes no yes 1 4 1
## 1.110 Male 15.06 7 yes no yes 1 4 1
## 1.2 Male 15.12 7 yes no yes 1 4 1
## 1.3 Male 15.18 7 yes no yes 1 4 1
## reason _yhat_ _vname_ _ids_ _label_
## 1 home 12.60381 sex 1 Tree
## 1.1 home 12.60381 sex 1 Tree
## 11 home 12.60381 age 1 Tree
## 1.110 home 12.60381 age 1 Tree
## 1.2 home 12.60381 age 1 Tree
## 1.3 home 12.60381 age 1 Tree
##
##
## Top observations:
## sex age paredu famsup activities romantic freetime health goout reason
## 1 Male 17 7 yes no yes 1 4 1 home
## _yhat_ _label_ _ids_
## 1 12.60381 Tree 1cp.rf## Top profiles :
## sex age paredu famsup activities romantic freetime health goout
## 1 Female 17.00 7 yes no yes 1 4 1
## 1.1 Male 17.00 7 yes no yes 1 4 1
## 11 Male 15.00 7 yes no yes 1 4 1
## 1.110 Male 15.06 7 yes no yes 1 4 1
## 1.2 Male 15.12 7 yes no yes 1 4 1
## 1.3 Male 15.18 7 yes no yes 1 4 1
## reason _yhat_ _vname_ _ids_ _label_
## 1 home 12.74237 sex 1 Random Forest
## 1.1 home 12.01880 sex 1 Random Forest
## 11 home 12.03692 age 1 Random Forest
## 1.110 home 12.03692 age 1 Random Forest
## 1.2 home 12.03692 age 1 Random Forest
## 1.3 home 12.03692 age 1 Random Forest
##
##
## Top observations:
## sex age paredu famsup activities romantic freetime health goout reason
## 1 Male 17 7 yes no yes 1 4 1 home
## _yhat_ _label_ _ids_
## 1 12.0188 Random Forest 1cp.gbm## Top profiles :
## sex age paredu famsup activities romantic freetime health goout
## 1 Female 17.00 7 yes no yes 1 4 1
## 1.1 Male 17.00 7 yes no yes 1 4 1
## 11 Male 15.00 7 yes no yes 1 4 1
## 1.110 Male 15.06 7 yes no yes 1 4 1
## 1.2 Male 15.12 7 yes no yes 1 4 1
## 1.3 Male 15.18 7 yes no yes 1 4 1
## reason _yhat_ _vname_ _ids_ _label_
## 1 home 12.63389 sex 1 GBM
## 1.1 home 11.74516 sex 1 GBM
## 11 home 11.73456 age 1 GBM
## 1.110 home 11.73456 age 1 GBM
## 1.2 home 11.73456 age 1 GBM
## 1.3 home 11.73456 age 1 GBM
##
##
## Top observations:
## sex age paredu famsup activities romantic freetime health goout reason
## 1 Male 17 7 yes no yes 1 4 1 home
## _yhat_ _label_ _ids_
## 1 11.74516 GBM 1plot(cp.lm, variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profile", "")
plot(cp.elastic, variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profile", "")
plot(cp.tree, variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profile", "")
plot(cp.rf, variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profile", "")
plot(cp.gbm, variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profile", "")
plot(cp.lm, cp.elastic, cp.tree, cp.rf, cp.gbm,color = "_label_",
variables = c("age", "paredu")) +
ggtitle("Ceteris-paribus profiles for Nesh", "") 
nesh.dat.1 <- data.frame(
sex = "Male",
age = 17,
paredu = 7,
famsup = "yes",
activities = "no",
romantic = "yes",
freetime = "1",
health = "4",
goout = "1",
reason = "home"
)
nesh.dat.2 <- data.frame(
sex = "Male",
age = 17,
paredu = 7,
famsup = "yes",
activities = "no",
romantic = "yes",
freetime = "1",
health = "4",
goout = "1",
reason = "reputation"
)
cp.elastic1 <- predict_profile(explainer = elastic.exp,
new_observation = rbind(nesh.dat.1,
nesh.dat.2))
plot(cp.elastic1 , color = "_ids_",
variables = c("age", "paredu")) +
scale_color_manual(name = "Nesh:", breaks = 1:2,
values = c("red", "blue"),
labels = c("home" , "reputation")) +
ggtitle("Ceteris-paribus profile elastic", "")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
cp.gbm1 <- predict_profile(explainer = gbm.exp,
new_observation = rbind(nesh.dat.1,
nesh.dat.2))
plot(cp.gbm1 , color = "_ids_",
variables = c("age", "paredu")) +
scale_color_manual(name = "Nesh:", breaks = 1:2,
values = c("red", "blue"),
labels = c("home" , "reputation")) +
ggtitle("Ceteris-paribus profile gbm", "")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
bd.elastic <- predict_parts(explainer = elastic.exp,
new_observation = nesh.dat,
type = "break_down")
bd.elastic## contribution
## Elastic: intercept 11.656
## Elastic: paredu = 7 0.730
## Elastic: sex = Male -0.547
## Elastic: goout = 1 -0.314
## Elastic: romantic = yes -0.167
## Elastic: health = 4 0.156
## Elastic: famsup = yes -0.148
## Elastic: activities = no -0.073
## Elastic: age = 17 -0.041
## Elastic: reason = home -0.039
## Elastic: freetime = 1 0.029
## Elastic: prediction 11.242plot(bd.elastic)
bd.gbm<- predict_parts(explainer = gbm.exp,
new_observation = nesh.dat,
type = "break_down")
bd.gbm## contribution
## GBM: intercept 11.628
## GBM: paredu = 7 0.852
## GBM: sex = Male -0.537
## GBM: goout = 1 -0.215
## GBM: health = 4 0.196
## GBM: age = 17 0.191
## GBM: romantic = yes -0.142
## GBM: famsup = yes -0.139
## GBM: reason = home -0.104
## GBM: freetime = 1 0.073
## GBM: activities = no -0.059
## GBM: prediction 11.745plot(bd.gbm)